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Identification of regulatory elements is essential for understanding the mechanism behind regulating gene expression. These regulatory elements—located in or near gene—bind to proteins called transcription factors to initiate the transcription process. Their occurrences are influenced by the GC-content or nucleotide composition. For generating synthetic coding sequences with pre-specified amino acid sequence and desired GC-content, there exist two stochastic methods, multinomial and maximum entropy. Both methods rely on the probability of choosing the codon synonymous for usage in regard to a specific amino acid. In spite the latter exhibited unbiased manner, the produced sequences are not exactly obeying the GC-content constraint. In this paper, we present an algorithmic solution to produce coding sequences that follow exactly a primary amino acid sequence and a desired GC-content. The proposed tool, namely CodSeqGen, depends on random selection for smaller subsets to be traversed using the backtracking approach. • Generating random coding sequence is computationally expensive. • For proteins of length n , we need to test 6 n coding sequences to find those satisfying GC-content constraints. • CodSeqGen uses smart backtracking, and produces synonymous coding sequences with the exact desired GC-contents. • Experiments show CodSeqGen produces sequences with GC-contents that satisfy the constraints while NullSeq does not. [ABSTRACT FROM AUTHOR]

Efflux proteins are the transport proteins expressed in the plasma membrane, which are involved in the movement of unwanted toxic substances through specific efflux pumps. Several studies based on computational approaches have been proposed to predict transport proteins and thereby to understand the mechanism of the movement of ions across cell membranes. However, few methods were developed to identify efflux proteins. This paper presents an approach based on the contextualized word embeddings from Bidirectional Encoder Representations from Transformers (BERT) with the Support Vector Machine (SVM) classifier. BERT is the most effective pre-trained language model that performs exceptionally well on several Natural Language Processing (NLP) tasks. Therefore, the contextualized representations from BERT were implemented to incorporate multiple interpretations of identical amino acids in the sequence. A dataset of efflux proteins with annotations was first established. The feature vectors were extracted by transferring protein data through the hidden layers of the pre-trained model. Our proposed method was trained on complete training datasets to identify efflux proteins and achieved the accuracies of 94.15% and 87.13% in the independent tests on membrane and transport datasets, respectively. This study opens a research avenue for the implementation of contextualized word embeddings in Bioinformatics and Computational Biology. [Display omitted] • Efflux proteins facilitate the movement of multi-drugs. • Efflux proteins transport harmful substances via particular efflux pumps. • Bidirectional Encoder Representations from Transformers to identify Efflux protein. • Contextual representations from BERT captures amino acids context in a sequence. • Cosine Similarity measures the degree of similarity between two vectors. [ABSTRACT FROM AUTHOR]

Abstract: Guanine nucleotides are key signaling molecules and many members of the G-protein family bind and hydrolyze nucleotides, particularly GTP, and regulate intracellular level of GTP and GDP. EngA is one of the members of these universally conserved GTPases. Amino acid sequence alignment of EngA of Mycobacterium tuberculosis H37Rv with other homologous bacterial proteins have shown that EngA of M. tuberculosis H37Rv has significant homology with EngA of other bacteria. EngA protein has shown GTP-binding and GTP hydrolysis activities as intrinsic biochemical properties of protein and this serves as a base to further investigate the physiological significance of this protein in the pathogenesis mechanism of M. tuberculosis H37Rv. In this paper for the first time EngA GTP-binding protein of M. tuberculosis H37Rv was functionally characterized for its GTPase and GTP-hydrolyzing activity. GTPases such as era, obg, lepA, and FtsZ are vital for growth and development and specifically cellular functions of bacteria, in view of these observations it can be concluded that EngA GTPase can be further utilized for the study of its functional role in the pathogenesis of M. tuberculosis H37Rv. [Copyright &y& Elsevier]

Abstract: Calmodulin (CaM) is a highly versatile Ca2+ signaling transducer known to regulate over a hundred proteins. In this paper, we further demonstrate the versatility of CaM binding by showing that it binds to a synthetic peptide (revCKKp) made by reversing the amino acid sequence of the CaM-binding peptide (CKKp) from CaM-dependent protein kinase kinase (CaMKK) (residues 438–463). Sequence comparison between revCKKp and other CaM-binding peptides (CBPs) from the CaM target databank showed that revCKKp does not resemble any existing classes of CBPs, except CKKp [M. Zhang, T. Yuan, Molecular mechanisms of calmodulin’s functional versatility, Biochem. Cell Biol. 76 (1998) 313–323; S.W. Vetter, E. Leclerc, Novel aspects of calmodulin target recognition and activation, Eur. J. Biochem. 270 (2003) 404–414]. Furthermore, computational modeling showed that revCKKp could bind CaM in a similar manner to CKKp. Lastly, we experimentally showed that our synthetic revCKKp binds to CaM in a reversible Ca2+-dependent manner. [Copyright &y& Elsevier]